Triple
T26915133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heptaméron |
E677496
|
entity |
| Predicate | intendedNumberOfStories |
P170961
|
FINISHED |
| Object | 100 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 100 | Statement: [Heptaméron, intendedNumberOfStories, 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedNumberOfStories Context triple: [Heptaméron, intendedNumberOfStories, 100]
-
A.
numberOfStories
Indicates the total count of levels or floors that a structure or building has.
-
B.
numberOfEmbeddedStories
Indicates the count of stories that are embedded within a given item or context.
-
C.
intendedNumberOfBooks
Indicates the number of books that an agent plans or aims to have, produce, read, or otherwise be associated with, as opposed to the number actually realized.
-
D.
storyNumber
Indicates the numerical identifier assigned to a specific story within a collection, sequence, or dataset.
-
E.
plannedNumberOfTests
Indicates the total count of tests that are intended or scheduled to be conducted for a given context or period.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69eee9bcef1c8190be88586bb902bb9b |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f6984bb55c8190862eb8796868d188 |
completed | May 3, 2026, 12:35 a.m. |
| PD | Predicate disambiguation | batch_69f69661e6ec8190948251c7516a32ad |
completed | May 3, 2026, 12:27 a.m. |
| PDg | Predicate description generation | batch_69f6978ec27c8190a488e1f9c2566d38 |
completed | May 3, 2026, 12:32 a.m. |
Created at: April 27, 2026, 6:04 a.m.